Data Weekends™ are accelerated data science workshop for programmers where you can quickly learn to apply predictive analytics to real-world data. We offer courses in Machine Learning and Deep Learning.

Our InstructorS: Francesco Mosconi

Francesco Mosconi is a Data Science consultant and trainer. With Catalit LLC he helps companies acquire skills and knowledge in data science and harness the power of machine learning and deep learning to reach their goals. Before Data Weekends, Francesco served as lead instructor in Data Science at General Assembly and The Data Incubator and he was Chief Data Officer and co-­founder at Spire, a YCombinator-­backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. He earned a joint PhD in biophysics at University of Padua and Université de Paris VI and is also a graduate of Singularity University summer program of 2011.

WHO SHOULD TAKE THIS COURSE?

Software Engineers and Analysts with previous coding experience in Python

People who are interested in learning more about data analysis in Python

Coders who are curious about machine learning, text processing, and prediction.

BENEFITS

All our courses offer a well balanced mix of 50% theory and 50% hands-on practice with the following activities:

Theory lectures

Individual mentorship, feedback and support

Hands-on coding labs

Build a prototype from start to finish in 2 days

Pair programming

Meet and learn with like-minded people

Case studies on real-world data and industry examples

Online resources

Network of recruiting partners

Testimonials

About us

Catalit

A Data Science consultancy doing what we love: helping companies and individuals acquire skills and knowledge in the field of data science and harness the power of machine and deep learning to reach their goals.

Zero to Deep Learning® International Bootcamp

A 1 week full-time training that brings you from Zero to Deep Learning® with Keras and Tensorflow. Built in partnership with San Francisco's Startup Basecamp (where you can actually stay!). You will move through the core Data Weekends curriculum, attend Bay Area meetups, and network with folks in the field. This course is the best way to get today's best knowledge and skills in data science, and is great for anyone who needs to learn in a hurry or who is from outside the area.

Machine Learning with Python, Pandas & Scikit-Learn

For people starting out with machine learning and looking to speed up their learning curve. This course provides a solid structure to organize your learning as well as code snippets and best practices. In this course you will learn to how to building, train and deploy machine learning models to predict continuous and discrete quantities.

Data Analytics with Python, SQL and Spark

For people who are getting started with data analytics and want to analyze small and large datasets. In this course you learn to process structured and unstructured data, extract meaningful insights and visualize them. The course is a great starting point for the more advanced courses in machine learning and deep learning.

Advanced Deep Learning with Python & Tensorflow

For people who are already familiar with the fundamentals of machine learning and deep learning. Building on the basic and intermediate courses you will explore more advanced topics. In this course you will learn to use Tensorflow to train supervised deep learning models and you will discuss advanced topics like custom losses, model serving and transfer learning. You will review case studies from public repositories and discuss industry applications of deep learning at scale.

Intro TO Deep Learning with Python & Keras

For people who already have a good understanding of machine learning and want quickly add deep learning to their toolbox. This course builds on top of the machine learning course providing examples and tools to use deep learning models on real world data. In this course you will learn about Fully Connected, Convolutional and Recurrent Neural networks and you will build models that work with images, text and numerical data

Custom Corporate Courses

For companies that want to run a course for their team we offer additional content and customized programs. These courses are offered through our parent company CATALIT LLC.

Reinforcement Learning with Python, Tensorflow & OpenAI

For people who are familiar with supervised deep learning and want to venture into unsupervised territory. In this course you will learn all about Q-Learning, Autoencoders and Adversarial Networks. You will train agents to play video games and apply unsupervised learning to a variety of problems.

Analytics to Reinforcement Learning 5 weekends bundle

WHAT MAKES US DIFFERENT?

We focus on Machine Learning and Deep Learning with Python.

Our courses are self-contained and hands-on. In 2 days you get to the core of the matter and learn enough foundations to bypass the painful part of approaching a new subject. Your learning curve will go from months to days.

Whether you are considering moving a career move, or simply want to include predictive modeling in your work, our workshops give you the tools to do so.

Why python?

In the last 2 years Python has become a de-facto standard in data science and it is widely adopted by most major companies. Reasons for this success include:

WHY Keras?

Keras is a high-level neural networks api and library that allows to simply build and train deep learning models using Tensorflow or Theano as backend. Written in Python it focuses on enabling fast experimentation. It recently became the preferred high level api for Tensorflow and it thus provides a great entry point to approach Tensorflow. Keras highlights:

Allows for easy and fast prototyping

Supports Fully connected, Convolutional and Recurrent..

Supports arbitrary connectivity schemes

Runs seamlessly on CPU and GPU

Integrates very well with Tensorflow and Tensorboard

Why Tensorflow?

There are many open source Deep Learning libraries. Tensorflow is backed by Google and is quickly becoming one of the most used libraries in the fields. It has a large and growing community of users and it is versatile and easy to learn. Highlights include

largest community of developers

state of the art models and nodes

high scalability, can be distributed on many GPUs

production performance and deployment tools

very versatile and powerful for distributed high performance computing beyond neural networks

WHY SPARK?

Apache Spark has revolutionized how we build and deploy data pipelines for ETL, Visualization and Machine Learning. Reasons for this success include:

Flexible enough to run SQL-style queries, machine learning algorithms, and everything in between

Fast and scalable: efficient memory use => runs up to 100x faster than Hadoop

Supports data exploration and production workflows => same code that works on a laptop can be deployed to cloud-based computing clusters